LLMs and Generative AI are the next steps forward for machine learning, the not-so-little engine that saved AI from the horror of technology irrelevance. Here, I note some similarities with today’s AI and yesterday’s Big Data – followed in a subsequent post by some observations from Andrew Ng, a machine learning pioneer, looking ahead to cost-effective use cases for the new tooling.
Similarity breeds comparison
A jaundiced view might hold that Genartive AI has taken over where Big Data left off. A rage a few years ago, Big Data fled from the scene. Is it anywhere now on a Gartner representation of the life of hype?
Big Data leaders, after they redefined recommendation engines and social media personalization, were often asked what Big Data was supposed to do next. The answer turned out to be “machine learning.” Flash forward to the present, and this has morphed into Large Language Models (LLMs) and prompt engineering.
There are plenty of differences between then and now. Let’s dwell on some similarities:
*As in the Big Data/Hadoop days of yesteryear, getting great gobs of custom data into the LLM is time consuming, labor intensive and error prone.
*The shiny new thingness may lure developers to chase the technology (which makes the resume sparkle) while short-changing the use case; that is, pursuing indefensible applications with a short and less-than-stellar commercial life spans.
*And, as with Big Data – and just about every innovation that has ever come about — what works as a prototype may fail to scale in production. As well, what worked for a small army of Google sysadmins may not work for you, or prove saleable either.
*The first tooling is raw, and development can become a trudge of semi-blind trial and error.
*There is a megaton bomb of hyperbole that explodes, followed by hemming-hawing, nitpicking and numb lethargy. See ‘Faded Love and Hadoop’.
These problems are familiar to innovators, but LLMs bring new classes of problems too. What some developers will find persistently annoying is a flakiness in interaction with the LLM. You can regularly prompt it with the same input, while getting different output. I asked Google Bard about this and the answer was: “Overall, whether or not prompt engineering is fun is up to you.”
Of course, a great effort is underway, and development teams will soon benefit from both the successes achieved and failures endured. Among the questions that should direct their efforts: Does the technology solve a widely-found problem of significant weight? In our next post, let’s find out what Andrew Ng says! – Jack Vaughan
| THIS IS PART 1 OF 2. FOR PART 2, GO TO Use cases ultimately pave Generative AI’s path: Face it! |